YOLOv7‐XAI: Multi‐Class Skin Lesion Diagnosis Using Explainable AI With Fair Decision Making

Abstract

Skin cancer, a prevalent and potentially life‐threatening condition, demands accurate and timely detection for effective intervention. It is an uncontrolled growth of abnormal cells in the human body. Studies are underway to determine if a skin lesion is benign (non‐cancerous) or malignant (cancerous), but the biggest challenge for a doctor is determining the type of skin cancer. As a result, determining the type of tumour is crucial for the right course of treatment. In this study, we introduce a groundbreaking approach to multi‐class skin cancer detection by harnessing the power of Explainable Artificial Intelligence (XAI) in conjunction with a customised You Only Look Once (YOLOv7) architecture. Our research focuses on enhancing the YOLOv7 framework to accurately discern 8 different skin cancer classes, including melanoma, basal cell carcinoma, and squamous cell carcinoma. The YOLOv7 model is the robust backbone, enriched with features tailored for precise multi‐class classification. Concurrently, integrating XAI elements, Local Interpretable Modal‐agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) ensures transparent decision‐making processes, enabling healthcare professionals to interpret and trust the model's predictions. This innovative synergy between YOLOv7 and XAI heralds a new era in interpretability, resulting in high‐performance skin cancer diagnostics. The obtained results are 96.8%, 94.2%, 95.6%, and 95.8%, evaluated with popular quantitative metrics such as accuracy, precision, recall, and F1 score, respectively.